Test alpha diversity for associations with metadata.

A convenience wrapper for adiv_table() + stats_table().

Usage

adiv_stats(
  biom,
  regr = NULL,
  stat.by = NULL,
  adiv = "Shannon",
  split.by = NULL,
  trans = "none",
  test = "emmeans",
  fit = "lm",
  at = NULL,
  level = 0.95,
  alt = "!=",
  mu = 0,
  p.adj = "fdr"
)

Arguments

biom

An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.

regr

Dataset field with the x-axis (independent; predictive) values. Must be numeric. Default: NULL

stat.by

Dataset field with the statistical groups. Must be categorical. Default: NULL

adiv

Alpha diversity metric(s) to use. Options are: "OTUs", "Shannon", "Chao1", "Simpson", and/or "InvSimpson". Set adiv=".all" to use all metrics. Default: "Shannon"

Multiple/abbreviated values allowed.

split.by

Dataset field(s) that the data should be split by prior to any calculations. Must be categorical. Default: NULL

trans

Transformation to apply. Options are: c("none", "rank", "log", "log1p", "sqrt"). "rank" is useful for correcting for non-normally distributions before applying regression statistics. Default: "none"

test

Method for computing p-values: 'wilcox', 'kruskal', 'emmeans', or 'emtrends'. Default: 'emmeans'

fit

How to fit the trendline. 'lm', 'log', or 'gam'. Default: 'lm'

at

Position(s) along the x-axis where the means or slopes should be evaluated. Default: NULL, which samples 100 evenly spaced positions and selects the position where the p-value is most significant.

level

The confidence level for calculating a confidence interval. Default: 0.95

alt

Alternative hypothesis direction. Options are '!=' (two-sided; not equal to mu), '<' (less than mu), or '>' (greater than mu). Default: '!='

mu

Reference value to test against. Default: 0

p.adj

Method to use for multiple comparisons adjustment of p-values. Run p.adjust.methods for a list of available options. Default: "fdr"

Value

A tibble data.frame with fields from the table below. This tibble object provides the $code operator to print the R code used to generate the statistics.

Field	Description
.mean	Estimated marginal mean. See emmeans::emmeans().
.mean.diff	Difference in means.
.slope	Trendline slope. See emmeans::emtrends().
.slope.diff	Difference in slopes.
.h1	Alternate hypothesis.
.p.val	Probability that null hypothesis is correct.
.adj.p	.p.val after adjusting for multiple comparisons.
.effect.size	Effect size. See emmeans::eff_size().
.lower	Confidence interval lower bound.
.upper	Confidence interval upper bound.
.se	Standard error.
.n	Number of samples.
.df	Degrees of freedom.
.stat	Wilcoxon or Kruskal-Wallis rank sum statistic.
.t.ratio	.mean / .se
.r.sqr	Percent of variation explained by the model.
.adj.r	.r.sqr, taking degrees of freedom into account.
.aic	Akaike Information Criterion (predictive models).
.bic	Bayesian Information Criterion (descriptive models).
.loglik	Log-likelihood goodness-of-fit score.
.fit.p	P-value for observing this fit by chance.

See also

Other alpha_diversity: adiv_boxplot(), adiv_corrplot(), adiv_table()

Other stats_tables: bdiv_stats(), distmat_stats(), stats_table(), taxa_stats()

Examples

    library(rbiom) 
    
    biom <- rarefy(hmp50)
      
    adiv_stats(biom, stat.by = "Sex")[,1:6]
#> # Model:    lm(.diversity ~ Sex)
#> # A tibble: 1 × 6
#>   Sex           .mean.diff .h1    .p.val  .adj.p .effect.size
#>   <chr>              <dbl> <fct>   <dbl>   <dbl>        <dbl>
#> 1 Male - Female      0.765 != 0  0.00992 0.00992        0.788
      
    adiv_stats(biom, stat.by = "Sex", split.by = "Body Site")[,1:6]
#> # Model:    lm(.diversity ~ Sex)
#> # A tibble: 5 × 6
#>   `Body Site`    Sex           .mean.diff .h1   .p.val .adj.p
#>   <fct>          <chr>              <dbl> <fct>  <dbl>  <dbl>
#> 1 Saliva         Male - Female     0.236  != 0   0.226  0.333
#> 2 Stool          Male - Female     0.243  != 0   0.228  0.333
#> 3 Buccal mucosa  Male - Female     0.541  != 0   0.250  0.333
#> 4 Anterior nares Male - Female     0.0440 != 0   0.831  0.831
#> 5 Mid vagina     NA               NA      NA    NA     NA    
    
    adiv_stats(biom, stat.by = "Body Site", test = "kruskal")
#> # Model:    kruskal.test(.diversity ~ `Body Site`)
#> # A tibble: 1 × 6
#>   .stat .h1         .p.val       .adj.p    .n   .df
#>   <dbl> <fct>        <dbl>        <dbl> <int> <int>
#> 1  38.7 > 0   0.0000000804 0.0000000804    49     4